Xkcd 1425 (Tasks) turns ten years old today

Bird vs. park: which task is actually harder now?

• Many note the comic’s reversal: bird detection is now “trivial” with off‑the‑shelf CNN/YOLO models; a few lines of code can detect birds in images.
• Others point out constraints the comic ignored: doing this on a 2014 phone, or without today’s models and tooling, would still have been hard.
• The “is this in a national park?” task hides complexity: GPS inaccuracy, bad reception, boundary ambiguity (rivers, cities in parks), changing geography, and unclear requirements for “in” vs “near” a park.
• GIS lookup itself (point‑in‑polygon against public park polygons) is technically straightforward now, but still depends on decades of prior work and datasets.

Progress in ML and shifting goalposts

• Commenters reflect on how impressive GANs and early convnets looked a decade ago versus today’s standards.
• Some argue expectations for AGI and the Turing test have been continuously “moved”; others say tests must naturally get stricter as systems improve.
• There’s debate whether current LLMs count as anything close to AGI, with some insisting core capabilities (strong reasoning, robust math, one‑shot learning) are still missing.

Turing test, “real thinking,” and philosophy

• Long sub‑thread on whether LLMs “really think” or just simulate language; references to Turing’s original framing, functionalism, and the Chinese Room.
• Some stress that questions like “does it think?” are partly semantic; others see deep qualitative gaps between human and model reasoning, despite superficial fluency.

Jobs, productivity, and “good enough” automation

• Disagreement on how many current jobs could be replaced today: some see many roles as trivially automatable; others say almost no jobs are fully replaceable yet.
• Concern that AI will be used where it’s cheaper but worse (translation, customer service), leading to degraded quality but higher profits.
• Others see AI enabling new jobs in places that previously couldn’t afford human labor at all.

Why similar‑looking software tasks differ by orders of magnitude

• Many emphasize the comic’s core point: non‑technical stakeholders often can’t tell which tasks are “move a chair” versus “move the toilet and plumbing.”
• House and plumbing analogies are used to explain why tiny‑sounding requirements (e.g., “robust MI,” “small UI tweak,” second optimistic‑update path) can imply major re‑architecture.

LLMs’ capabilities and sharp edges

• LLMs excel at data cleanup, metadata normalization, simple CV (vision APIs), and rapid prototyping; they’re described as “ultimate interns.”
• Failure modes are highlighted: hallucinations, weak arithmetic, reluctance to say “I don’t know,” and trouble with tokenization‑sensitive tasks (spelling “STRAWBERRY,” rendering “HELLO” in vegetables, negation like “no cheese”).
• Some see these as inherent to current token‑based architectures; others think better training, tools, or hybrid systems can mitigate many issues.

Infrastructure, maturity, and “easy vs hard”

• Multiple comments stress that both tasks (GPS/GIS and bird detection) became “easy” only after huge investments in satellites, GIS standards, datasets, GPUs, and ML research.
• The comic is read less as a statement about vision per se and more as a reminder that perceived vs actual difficulty in software depends heavily on invisible infrastructure and historical context.